Edge security protects data at the Edge
It’s no secret that Edge computing and Edge AI technologies are growing fast.
This growth is taking place because Edge computing and Edge AI are on their way to becoming indispensable technologies due to their ability to move data away from overburdened cloud data centers.
Security concerns have also drastically increased with the rapid uptake of Edge technologies, but Edge security is here to help secure devices that use Edge AI.
Mobile and IoT applications are exploding right now, creating the need for low-latency, secure, easily scalable platforms capable of processing enormous amounts of data.
While Edge computing and Edge AI have advantages over cloud computing and cloud-based AI when it comes to this, additional measures can be taken to boost the security of Edge technologies, calming anxieties around this new tech.
This is where Edge security comes in. It’s aimed at protecting data that lives or travels through Edge-enabled devices.
To understand Edge security, you first need to understand Edge computing
Before we explore what Edge security is and why it’s essential, it’s important to understand Edge computing and its relationship to AI and the IoT.
1. Edge computing provides local, decentralized compute power
Instead of depending on a centralized data center for storing, processing, and distributing data, Edge computing takes care of these functions close to where data is initially generated.
Cloud data centers can be located hundreds or even thousands of miles away from end users resulting in latency issues and privacy problems.
On the other hand, with Edge computing, processing and storage can occur near or on the device where the information was gathered.
When processing is done at the Edge, only significant data is transferred. This reduces (or outright abolishes) latency, which is particularly important in use cases where the immediate transfer of information is required.
2. Edge computing empowers IoT technology
By catering to an IT architecture that’s spread out, Edge computing makes real-time computing for remote, global workforces a reality.
Thanks to Edge computing, IoT devices and smart apps can respond to data almost instantly, allowing businesses to keep their promises of delivering quick and stable access to apps and services.
3. Edge computing positively impacts AI
With Edge AI, responses can be delivered almost instantly. And, when AI is working on a device, security is enhanced as files don’t need to be passed around a network for analysis.
Edge security explained
Despite the security benefits inherent to Edge solutions, the growth of Edge computing and Edge AI has brought a different set of security concerns, prompting the need for new approaches.
Mobile and IoT apps are the chief drivers of Edge computing and, by extension, Edge security. Due to the growth of IoT devices, there is a demand for solutions that can process vast amounts of data while still delivering security.
Edge security refers to enterprise security for corporate resources that are outside the safe borders of a centralized data center. This type of security safeguards apps and users located at the Edge of a company’s network, where data is susceptible to security threats.
The concept of a Secure Access Service Edge (SASE) also comes into play here and refers to a category of services and hardware used to empower Edge security.
The term was coined by Gartner, an international advisory and research firm, in 2019.
Gartner explains that SASE is a rising force that can effectively combine extensive network security functions and comprehensive WAN capabilities.
In the same year, the term was coined, Gartner predicted that by 2024, almost fifty percent of enterprises would adopt SASE strategies.
What are the 3 most common Edge security risks?
Now that we’ve defined Edge security and SASE, let’s explore why businesses feel safeguarding the Edge is necessary.
1. Engaging with the network can be risky for Edge-enabled devices
One significant issue surrounding Edge computing is how Edge-enabled devices need to engage with the network but might not be safeguarded by that network.
When using Edge computing and Edge AI, the technology within the device itself manages sensitive data, and a device on its own may not be as secure as the network.
Another issue is that the network can be at risk if an Edge computing or Edge AI system is compromised.
These risks can be lessened by combining logical and physical security measures.
Logical security measures involve establishing solid authorization and authentication controls and data encryption. In contrast, physical security measures include guaranteeing that the device is well-secured and seeing that only authorized individuals have physical access.
2. Edge Devices bring cybersecurity concerns
When company data is kept and accessed by devices at the Edge, there can be a higher cybersecurity risk.
For Edge devices created without built-in security, malware becomes a real problem.
To make matters worse, many off-the-shelf Edge devices either don’t have the necessary security features or aren’t updated as regularly as they should be.
Machine learning (ML) models that power Edge AI systems can also be tampered with if the correct security measures aren’t in place.
If criminals can find ways to exploit loopholes in the technology, systems that depend on Edge AI capabilities (such as surveillance cameras) can become significant security threats.
Locking down machine learning models by treating them as primary assets that need protection will be vital in avoiding cyberattacks. It is also important to ensure that Edge AI algorithms are monitored and updated regularly.
3. The availability of Edge technology makes attacks easier
Since Edge technology is becoming readily available, and many Edge-enabled devices are relatively inexpensive – almost anyone can get hold of the technology and analyze it for vulnerabilities.
When criminals have access to Edge AI-enabled devices, creating malicious software designed to compromise the technology is much easier.
Because of this, Edge devices are at greater risk of attack.
Edge Security and the IoT
Edge computing and Edge AI have several use cases, but one significant use case is enhancing service quality for IoT devices.
Thanks to IoT devices, an enormous number of entry points at the Edge contribute to its vulnerability. And the number of IoT devices in action keeps growing, worsening the situation. Not to mention that many IoT devices don’t have good security features.
As mentioned earlier, not all organizations that manufacture IoT devices properly secure them. This means the networks themselves must be secured to make up for this.
The good news is that organizations can take back control by programming specific security measures into Edge nodes that can work as micro data centers with more processing power and greater security features. In these instances, any traffic transmitted from compromised IoT devices is flagged as corrupt and cannot access the network.
We’ll explore how to secure the Edge in more detail in the next section, but for now, it’s important to know that Edge security is becoming more and more popular because of the escalation of Edge computing and Edge AI-enabled IoT devices.
The top 8 ways to secure the Edge
Given that Edge AI can put networks at risk, let’s explore some methods organizations can take to mitigate the problem.
1. Companies need to introduce standards at the Edge
Since Edge AI systems are still relatively new, one of the most significant risks is the inadequacy of standards for Edge AI development and processes.
Having clear standards aids in the protection of a device’s security and network and defines a handy set of guidelines that businesses can follow moving forward.
2. Early threat detection helps to secure the Edge
Since Edge computing isn’t centralized, providers must individually put into practice threat detection tech that can effectively detect potential breaches before they cause any significant damage and data loss.
3. Vulnerability management is a must for Edge computing and Edge AI
Maintenance and looking out for known and unknown vulnerabilities must be done continuously when considering Edge computing and Edge AI.
4. Edge solutions require excellent perimeter security
Perimeter security can include firewalls and encryption tunnels to secure access to Edge computing resources.
5. Applications security is essential for Edge computing and Edge AI
To be safe, apps that are run on Edge computing and Edge AI devices must be secured beyond the network layer.
6. Patch cycles can help protect devices at the Edge
Patch management must be implemented to keep devices updated and lessen the chance of surface attacks.
Patch management refers to distributing and applying software updates to correct bugs and address other vulnerabilities.
7. Encryption is an essential component of Edge security
If companies aim to offer good security at the Edge, they first need to ensure that all the data going through the company’s endpoints (and all the data stored on company devices) is encrypted.
How can businesses secure the Edge?
But, like all technologies, Edge computing and Edge AI also have possible security risks.
When executing Edge computing, vulnerabilities must be detected beforehand, after which they must be dealt with to secure the system and block off malicious attacks.
To summarize, broadly speaking, a good Edge security strategy needs to take into account the following:
- Encryption of data that is in transit or at rest.
- Restricted access to the network and data resources.
- Automated monitoring tools.
On top of this, SASE architectures (which address today’s most common security problems due to applications living outside data centers) can offer further peace of mind.
The IoT increasingly depends on Edge networks; however, there are some genuine security risks concerning Edge networks and IoT devices, making Edge security an essential technology in securing the IoT’s future.